Papers by Tomer Levinboim
Informative Image Captioning with External Sources of Information (P19-1)
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| Challenge: | Current captioning models are trained to generate captions that only contain common object names, thus falling short on an important “informativeness” dimension. |
| Approach: | They propose a mechanism for integrating image information and fine-grained labels into a caption that describes the image in a fluent and informative manner. |
| Outcome: | The proposed model integrates image information with fine-grained labels to produce fluent captions . it can control the appearance of these labels in the output, resulting in fluent and informative captions. |
Quality Estimation for Image Captions Based on Large-scale Human Evaluations (2021.naacl-main)
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| Challenge: | a problem with automatic image captioning is that it produces low quality captions when used in the wild. |
| Approach: | They propose to model caption quality from a human perspective and *without* access to ground-truth references. |
| Outcome: | The proposed model can detect and filter out low-quality captions on previously unseen images. |